Fault lines after the Cold War: the vertical expansion of the concept of security, securitization and human security

The paper approaches the “vertical expansion of the concept of security” reconstructing the debate on the concept of security within the discipline of International Relations after the Cold War. Considering that security is an “essentially contested concept”, it offers a handful of comparisons between different conceptions, which provide different accounts of “broadening” security. Barry Buzan’s Securitization approach was the first to engage seriously the challenges of “broadening” security in IR. For its merits, however, Buzan’s communitarian ontology poses a problem to “broadening” security, as it reiterates the state as the gatekeeper of protection and as the authoritative site for defining existential threats. In this sense, in spite of all its overriding ambiguity, Human Security provides a better alternative for the “vertical expansion of the concept of security” than securitization. The paper, therefore, considers the respective contributions of securitization and human security to the debate on the vertical expansion of security under the light of the relationship between states and human beings

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

[pt] MUDANÇAS INSTITUCIONAIS NAS ATIVIDADES RELATIVAS ÀS OPERAÇÕES DE MANUTENÇÃO DA PAZ DO SISTEMA ONU DO PÓS-GUERRA FRIA: ADAPTAÇÃO VERSUS APRENDIZADO

O presente trabalho se propõe a aferir a mudança institucional que teve lugar nas atividades da Organização das Nações Unidas (ONU) relacionadas com as Operações de Manutenção da Paz, no período 1992- 2000. A abordagem levada a cabo se localiza no encontro da disciplina das Relações Internacionais com outras Ciências Sociais, através dos autores Ernst Haas e Anthony Giddens. Na Modernidade, organizações sociais, como a ONU, levam a cabo contínuo monitoramento, reflexivo, de suas próprias ações, na busca por solucionar problemas de cuja solução são incumbidos. Nessa dinâmica de monitoramento reflexivo, as organizações sociais podem - ou não - aprender com suas experiências pregressas.The present research intends to evaluate institutional change that had taken place within United Nations (UN) activities related to Peacekeeping operations, from 1992 to 2000. The following approach is located on the interface between International Relations and other Social Sciences, through the theoretical contributions of Ernst Haas and Anthony Giddens. In Modernity, social organizations (such as UN) continuously monitor its own behavior - reflexively - seeking for solutions for problems to be solved. Amidst this dynamics of reflexive monitoring, social organizations may - or may not - learn from its previous experiences